System and method for managing supply of service

ABSTRACT

Disclosed herein are a system and a method for managing supply of service. The system may include at least one processor that performs the operations including receiving a plurality of orders for a service; marking a locus based on the plurality of orders, the marked locus relating to a first number of orders of the plurality of orders, the first number of orders sharing a first characteristic, and the marked locus relating to a first location; and identifying at least one provider of the service to whom information relating to the marked locus is to be delivered.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Application No.201410168588.1 filed on Apr. 24, 2014, Chinese Application No.201410366721.4 filed on Jul. 29, 2014, and Chinese Application No.201510037388.7 filed on Jan. 23, 2015, the entire contents of each ofwhich are incorporated herein by reference.

TECHNICAL FIELD

This application relates generally to management of supply of service,and in particular, management of supply of service using anetwork-based, e.g., Internet-based, system and method.

BACKGROUND

On-demand services, such as fleet management systems employed for taxiand limousine fleets, typically use onboard metering devices, radios,and cell phones to dispatch drivers. Such a system typically is notcommunicative to or does not monitor the distribution of customers thatare waiting for pickup.

SUMMARY

This application relates generally to management of supply of service,and in particular, management of supply of service using anetwork-based, e.g., Internet-based, system and method. A systemdisclosed herein may identify areas that are under-served or over-servedbased on the distribution of service requesters, service providers, orthe like, or a combination thereof.

In one example, a system having at least one processor, storage, and acommunication platform is provided for managing supply of service. Thesystem includes a collection module configured to receive a plurality oforders for a service, an identification module configured to mark alocus based on the plurality of orders, and a determination moduleconfigured to identify at least one provider of the service to whominformation relating to the marked locus is to be delivered. The markedlocus relates to a first number of orders of the plurality of orders,the first number of orders sharing a first characteristic. The markedlocus relates to a first location. The first characteristic may be thata distance between the first location and a location relating to anorder of the marked locus is less than a first threshold. The collectionmodule may be configured to receive at least one piece of informationselected from the group consisting of, e.g., an order location relatingto an order of the plurality of orders, a provider location relating toa provider, an order acceptance rate relating to the plurality oforders, an order acceptance rate relating to the marked locus, a trafficcondition relating to the marked locus, a road condition relating to themarked locus, a weather condition relating to the marked locus, andhistorical information relating to the marked locus.

The collection module may further include a location informationcollector configured to identify the order location relating to an orderof the plurality of orders. The location information collector mayinclude a receiver configured to communicate with a positioning devicerelating to the order. In one embodiment, at least one order of theplurality of orders includes location information of a user. Thelocation information may be determined based on a positioning signalfrom a device associated with the user. At least one order of theplurality of orders may be received through a network, e.g., theInternet.

The identification module may be configured to identify a locus. Thelocus may be identified based on one or more cluster algorithm. Theidentification module may include at least one unit selected from e.g.,a historic information processor, and an order information processor, aprovider information processor, a contingent information processor, orthe like, or a combination thereof. The order information processor orthe provider information processor may include at least one unitselected from e.g., a location information processor, a distancecalculator, a time calculator, a miscellaneous information unit, or thelike, or a combination thereof. The information processor may include atleast one unit selected from, e.g., a location information processor, adistance calculator, a time calculator, a miscellaneous informationunit, or the like, or a combination thereof. The cluster algorithm mayinclude CLARANS, PAM, CLATIN, CLARA, DBSCAN, BIRCH, OPTICS, WaveCluster,CURE, CLIQUE, K-means algorithm, hierarchical algorithm, or the like, ora combination thereof. The identification module may be furtherconfigured to identify a second number of providers relating to themarked locus. The second number of providers share a secondcharacteristic. The second characteristic may be that a distance betweenthe first location and a location relating to a provider of the secondnumber of providers is less than a second threshold. In one embodiment,the first characteristic (that the distance between the first locationand a location relating to an order of the first number of orders isless than a first threshold) and the second characteristic may be thesame, and the first threshold and the second threshold may be the same.The first number of orders and the second number of providers may belocated in the same locus or region. The identification module may beconfigured to mark the locus based on a determination based on the ratioof the first number to the second number. For instance, theidentification module may be configured to mark the locus based on thedetermination that the ratio of the first number to the second numberexceeds a third threshold. In another embodiment, the identificationmodule is further configured to identify an area. The identificationmodule may be configured to mark locus based on one or more ordersrelate to the area. In another embodiment, the identification module maybe configured to identify or mark the locus based on a determinationthat the first number or the second number exceeds a fourth threshold.In an embodiment, an area may be identified as a locus, and be markedsuch that the information relating to the marked locus is delivered toone or more requesters, one or more providers, or the like, or acombination thereof. The criteria for identifying a locus may be thesame as the criteria for marking a locus. Merely by way of example, anarea is identified as a locus if the number of orders with the areareaches or exceeds a threshold. The identified locus is marked such thatthe information relating to the marked locus is delivered to one or morerequesters, one or more providers, or the like, or a combinationthereof. In another embodiment, an area may be identified based on afirst criterion (or criteria), and the identified locus that satisfies asecond criterion (or criteria) is marked such that the informationrelating to the marked locus is delivered to one or more requesters, oneor more providers, or the like, or a combination thereof. The firstcriterion (or criteria) may be different from the second criterion (orcriteria). Merely by way of example, an area is identified as a locus ifthe number of orders with the area reaches or exceeds a threshold. Theidentified locus is marked if the ratio of the number of orders withinthe area to the number of providers within the same area exceeds anotherthreshold. The information relating to the marked locus is delivered toone or more requesters, one or more providers, or the like, or acombination thereof.

The determination module may be configured to determine to whom theinformation relating to a marked locus is delivered. The determinationmodule may include at least one unit selected from e.g., a historicinformation processor, and an order information processor, a providerinformation processor, a contingent information processor, or the like,or a combination thereof. In still another embodiment, the systemfurther comprises a delivery module configured to deliver theinformation relating to the marked locus to a requester relating to anorder of the marked locus, or to the at least one provider.

In another example, a system having at least one processor is provided.The at least one processor performs the operations including, e.g.,receiving a plurality of orders for a service; marking a locus based onthe plurality of orders, the marked locus relating to a first number oforders of the plurality of orders, the first number of orders sharing afirst characteristic, and the marked locus relating to a first location;and identifying at least one provider of the service to whom informationrelating to the marked locus is to be delivered. The system is adaptedfor managing supply of the service. The first characteristic may be thata distance between the first location and a location relating to anorder of the marked locus is less than a first threshold. In oneembodiment, the system may perform the operations of communicating witha positioning device relating to an order of the plurality of orders;and identifying the order location relating to the order. In anotherembodiment, the system may perform, by or on the at least one processor,the operations of receiving at least one order from a network. In afurther embodiment, the system may perform, by or on the at least oneprocessor, the operation of identifying the marked locus based on atleast one cluster algorithm. Exemplary cluster algorithm is describedelsewhere in the present teachings. In still another embodiment, thesystem may perform, by or on the at least one processor, identifying asecond number of providers relating to the marked locus. The secondnumber of providers may share a second characteristic. The secondcharacteristic may be that a distance between the first location and alocation relating to a provider of the second number of providers isless than a second threshold. In one embodiment, the firstcharacteristic (that the distance between the first location and alocation relating to an order of the first number of orders is less thana first threshold) and the second characteristic may be the same, andthe first threshold and the second threshold may be the same. The firstnumber of orders and the second number of providers may be located inthe same locus or region. The marking the locus may includingdetermining the ratio of the first number to that second number. Forinstance, the locus is marked when the ratio of the first number to thesecond number exceeds a third threshold. In an embodiment, the markingthe locus includes determining that the first number, or the secondnumber exceeds a fourth threshold. In an embodiment, the marking thelocus includes determining that the first number, or the second numberexceeds a fourth threshold. In an embodiment, the system may perform, byor on the at least one processor, the operations of identifying an areaas a locus, and/or marking the locus such that the information relatingto the marked locus is delivered to one or more requesters, one or moreproviders, or the like, or a combination thereof. The criteria foridentifying a locus may be the same as the criteria for marking a locus.In another embodiment, an area may be identified based on a firstcriterion (or criteria), and the identified locus that satisfies asecond criterion (or criteria) is marked such that the informationrelating to the marked locus is delivered to one or more requesters, oneor more providers, or the like, or a combination thereof. The firstcriterion (or criteria) may be different from the second criterion (orcriteria). The system may perform, by or on the at least one processor,the operation of delivering the information relating to the marked locusto a requester relating to an order of the marked locus, or to the atleast one provider. The delivering may be performed by a device outsideof, or independent from the system.

In a further example, a method implemented on at least one processor isprovided. The method includes receiving, by or on the at least oneprocessor, a plurality of orders for a service; marking, by or on the atleast one processor, a locus based on the plurality of orders, themarked locus relating to a first number of orders of the plurality oforders, the first number of orders sharing a first characteristic, andthe marked locus relating to a first location; and identifying, by theat least one processor, at least one provider of the service to whominformation relating to the marked locus is to be delivered. The methodmay include identifying an area as a locus, and/or marking the locussuch that the information relating to the marked locus is delivered toone or more requesters, one or more providers, or the like, or acombination thereof. The criteria for identifying a locus may be thesame as the criteria for marking a locus. In another embodiment, an areamay be identified based on a first criterion (or criteria), and theidentified locus that satisfies a second criterion (or criteria) ismarked such that the information relating to the marked locus isdelivered to one or more requesters, one or more providers, or the like,or a combination thereof. The first criterion (or criteria) may bedifferent from the second criterion (or criteria). The method mayinclude delivering the information relating to the marked locus to arequester relating to an order of the marked locus, or to the at leastone provider, or the like, or a combination. The method is adapted formanaging supply of the service.

In still a further example, a method implemented on at least oneprocessor is provided. The method includes receiving a first order and asecond order, the first order comprising a first order time, a firstorigin, and a destination, the second order comprising a second ordertime and a second origin; calculating a first time to reach thedestination based on the first order time, the first origin, and thedestination; determining a first difference between the destination andthe second origin; determining a second difference between the firsttime and the second order time; and marking, if the first difference isless than a first threshold and the second difference is less than asecond threshold, the first order and the second order. The method isadapted for managing the first order and the second order.

Any one of the thresholds described above may be a constant, or avariable. Merely by way of example, a threshold may vary based on, e.g.,the time of the day, the day of the week, the road condition, thetraffic condition, a specific condition specified by a requester or aprovider, or the like, or a combination thereof. A threshold may be apredetermined constant or a predetermined variable. For instance, thethreshold may be a variable as a function of time, a function of acontingent condition, or a function of two or more parameters, or thelike. The function may be derived from, e.g., historical informationusing a machine-learning algorithm. An exemplary machine learningalgorithm may be one of supervised learning, unsupervised learning,semi-supervised learning, reinforcement learning, or the like, or acombination thereof. An exemplary machine learning algorithm may bec4.5, k-Means, Support Vector Machines (SVM), Apriori, ExpectationMaximization (EM), PageRank, AdaBoost, k-Nearest Neighbors (kNN), NaiveBayes, Classification and Regression Tree (CART), or the like, or acombination thereof.

Other concepts relate to software for implementing the presentteachings. A software product, in accord with this concept, includes atleast one machine-readable non-transitory medium and information carriedby the medium. The information carried by the medium may be executableprogram code data, parameters in association with the executable programcode, and/or information relating to a service requester, a serviceproviders, various information relating to the service of interest, themanagement of supply of the service of interest, etc.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present teachings may be realized and attained by practice or use ofvarious aspects of the methodologies, instrumentalities and combinationsset forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, systems, and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 illustrates an exemplary system configuration in which ascheduling system may be deployed in accordance with various embodimentsof the present teachings;

FIG. 2 depicts an exemplary diagram of the scheduling system of thesystem configuration illustrated in FIG. 1, according to an embodimentof the present teachings;

FIG. 3-a and FIG. 3-b are flowcharts of two exemplary processes in whicha scheduling system is deployed, according to an embodiment of thepresent teachings;

FIG. 4 is a block diagram illustrating an architecture of a collectionmodule according to an embodiment of present teachings;

FIG. 5 is a block diagram illustrating an architecture of theorder/provider information unit according to an embodiment of presentteachings;

FIG. 6 is a block diagram illustrating an architecture of the locationinformation collector according to an embodiment of present teachings;

FIG. 7 is a diagram illustrating a collection module configured forreceiving information from various sources or devices according to anembodiment of the present teachings;

FIG. 8 is a block diagram of the identification module according to anembodiment of the present teachings;

FIG. 9 depicts an exemplary diagram of an order information processoraccording to an embodiment of the present teachings;

FIG. 10-a and FIG. 10-b illustrate the exemplary diagrams of the lociidentification according to one embodiment of present teachings;

FIG. 11 is a diagram illustrating how a locus/region partition algorithmin the identification module input and output according to oneembodiment of present teachings;

FIG. 12 is another diagram illustrating how a specified region partitionalgorithm in the identification module input and output according to oneembodiment of present teachings;

FIG. 13 is a flowchart diagram of the Dbscan clustering algorithmaccording to another embodiment of present teachings;

FIG. 14 is a flowchart illustrating the identification module forfurther marking the locus according to an embodiment of the presentteachings;

FIG. 15 is a flowchart of an exemplary process of deliveringadvertisement to service providers, according to an embodiment of thepresent teachings;

FIG. 16 is a flowchart of another exemplary process of deliveringadvertisement to service providers, according to an embodiment of thepresent teachings;

FIG. 17 depicts the architecture of a mobile device which may be used toimplement a specialized system incorporating the present teaching;

FIG. 18 depicts the architecture of a computer which may be used toimplement a specialized system incorporating the present teaching; and

FIG. 19 is a diagram illustrating the correlation betweenprovider-requester ratio and the order acceptance rate according to anembodiment of the present teachings.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relatively highlevel, without detail, in order to avoid unnecessarily obscuring aspectsof the present teachings.

The present teachings describe method, system, and programming aspectsof a service system to provide service information which identifiesareas that are under-served or over-served by service providers. Themethod and system as disclosed herein aim at identifying thedistribution pattern of, e.g., orders, service providers, or the like,or a combination thereof, and mark an area or region where there is amismatch between the demand and supply of a service, and provide suchinformation to service requesters, service providers, or the like, or acombination thereof. The regions of interest may be identified based onvarious algorithms or other criteria in different situations with realtime and/or historic information, or other information. The efficiencyof the service may be improved as service providers or requesters maymake adjustments, e.g., moving to different regions or areas to get orprovide service, based on this information.

The system and method for managing supply of service may be used indifferent transportation system (transportation includes but is notlimited to land transportation, sea transportation and airtransportation, or the like, or a combination there of) including, suchas fleet management system employed for taxi and limousine fleets,intra-city express delivery system, or the like. It is understood thatthese exemplary applications of the system and method disclosed hereinare provided for illustration purposes, and not intended to limit thescope of the present teachings. The disclosed system and method may beapplied in other contexts, e.g., other on-demand services.

In the present teachings, a “user,” a “passenger,” a “requester,” a“service requester,” and a “customer” are used interchangeably to referto individuals that are requesting or ordering a service. Also, a“provider,” a “service provider,” and a “supplier” are usedinterchangeably to refer to an individual, an entity or a tool that mayprovide a service or facilitate the providing of the service. Also, a“locus,” a “cluster,” and a “group” are used interchangeably to refer toa group of similar objects sharing a certain characteristic. In someembodiment, a “locus” or a “cluster” may relate to a certain region.Merely by way of example, a locus may refer to a plurality of orders ina region, and may also refer to the region. In some embodiment, a regionmay relate to a plurality of objects that may be seen as a locus or acluster.

FIG. 1 is a diagram illustrating an exemplary system configuration 100in which a scheduling system may be deployed in accordance with variousembodiments of the present teachings. The exemplary system configuration100 includes a scheduling system 102, service requesters 104, a user logdatabase (DB) 106, service providers 108, and a network 114. The network114 may be a single network or a combination of different networks. Forexample, the network 114 may be a local area network (LAN), a wide areanetwork (WAN), a public network, a private network, a wireless localarea network (WLAN), a virtual network, a Metropolitan Area Network(MAN), a Public Telephone Switched Network (PSTN), or any combinationthereof. The network 114 may also include various network access points,e.g., wired or wireless access points such as base stations or Internetexchange points 114-1, 114-2 . . . , through which a data source mayconnect to the network 114 in order to transmit information via thenetwork 114.

The users 104 from whom orders for services may be placed may be ofdifferent types, such as users connected to the network 114 via adesktop computer 104-1, a laptop computer 104-2, a built-in device in amotor vehicle 104-3, or a mobile device 104-4. A user or requester maysend a request and receive results or suggestions via the network 114.The scheduling system (or referred to as system) 102 may accessinformation stored in the User Log DB (Database) 106 or directly via thenetwork 114.

The User Log DB 106 may be generated by one or more differentapplications (not shown), which may be running at the backend of thescheduling system, or as a completely standalone system capable ofconnecting to the network 114, accessing information from differentsources, analyzing the information, generating structured information,and storing such generated information. As illustrated in FIG. 1, theUser Log DB 106 may be connected to the network 114 and the schedulingsystem 102. In some embodiments, there is at least one gateway betweenthe User Log DB 106 and the network 114, and an authentication is neededbefore a user, a provider, or a third party may get access to the UserLog DB 106 through the network 114. The service providers 108 mayinclude multiple service providers 108-1, 108-2, 108-n, such asdifferent types of vehicles for hire. For example, a service providermay correspond to a taxi company, a single taxicab, a registered privatecar, or a vehicle with a registered driver. Various types of serviceproviders have registered with the scheduling system 102 so that theymay communicate with the schedule system 102 to exchange information.For example, the scheduling system may access information of serviceproviders, information of service requesters, information of orders forservices, or may send notifications or advertisements to serviceproviders, service requesters, or the like, via the network 114.

FIG. 2 is a block diagram of scheduling system 102 of the systemconfiguration shown in FIG. 1. The scheduling system 102 includes acollection module 210, an identification module 220, a determinationmodule 230, a delivery module 240, and a historical information DB 250.The collection module 210 may collect information relating to therequesters 104, the providers 108, or the like, or a combinationthereof. The collection module 210 may collect information through thenetwork 114 and/or the User Log DB 106. Also, the collection module 210may collect contingent information or environmental information from oneor more contingent information sources 260. Contingent information mayinclude, but not limited to, a weather condition, a road condition, atraffic condition, a provider-requester ratio, an order acceptance rate,or the like, or a combination thereof. The historical informationdatabase (DB) 250 may store historical information relating to, e.g.,customers, providers, orders distribution, providers distribution, thedemand and supply relationship, or the like, or a combination thereof.The identification module 220 may receive information from, e.g., thecollection module 210, and identify or mark loci of orders based on thereceived information. Information relating to the identified or markedloci of orders may be processed further. Merely by way of example, theidentification module 220 may mark whether a locus or region isunder-served or over-served, based on order related information,provider related information, contingent information inside or aroundthe locus or region. In one embodiment, information regarding anidentified or marked locus or region from the identification module 220maybe directly sent to the delivery module 240. In another embodiment,the determination module 230 may be configured to receive informationfrom the identification module 220, and identify to whom the informationrelating to an identified or marked locus or region may be sent. Thedetermination module 230 may send feedback to the identification module220. Information relating to, e.g., a locus or region is sent to thedelivery module 240. The delivery module 240 may deliver information,such as an advertisement, an announcement, or guidance, to requesters,providers, a third party, or the like, or a combination thereof. Theinformation relating to an identified or marked locus or region may alsobe sent to the determination module 230, the identification module 220,or the historical information DB 250 as feedback, or for backup orstorage.

It should be noted that it is possible to implement a different system102 having more or fewer constituent modules than those of FIG. 2 asneeded. While the foregoing has described what are considered toconstitute the present teachings and/or other examples, it is understoodthat various modifications may be made thereto and that the subjectmatter disclosed herein may be implemented in various forms andexamples, and that the teachings may be applied in numerousapplications, only some of which have been described herein. Thoseskilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,some of the above-described collection module 210, the identificationmodule 220, the determination module 230, or the delivery module 240 maybe embodied in a single module or device, or a single module or devicemay conduct the functions of two or more of the mentioned modules. Forexample, a module may be used both to receive related information and tomark a locus, as achieved by the collection module 210 and theidentification module 220 described above.

FIG. 3-a is a flowchart of an exemplary process in which a schedulingsystem 102 is deployed, according to an embodiment of the presentteachings. Beginning at block 301, information is received. In oneembodiment, the received information may include information relating toorders, requesters, providers, historical information, contingentinformation, or the like, or a combination thereof. In one embodiment,at least some of the received information is real-time information. Asused herein, real-time information refers to that at the time or aroundthe time (e.g., within several seconds, within several minutes, etc.) anorder is made, or at a time of interest. As used herein, historical (orhistoric) information may include past information relating to, e.g.,the demand or supply of a service (including same or similar services)in an area or region. For instance, the historic information may includethe number of orders, the number of providers, the order acceptancerate, the traffic condition, the road condition, or the like, or acombination thereof. The historical information may include informationover a period of time. As another example, the historical informationmay include a profile of any past information exemplified above as afunction of time, e.g., the variation at different times within a day(e.g., rush hours, off-peak hours, or the like), for different days of aweek, or the like, or a combination thereof. The historical informationmay be used for predicting or deriving information for a time pointlater than when the historical information is generated, or when theunderlying events associated with the historical information occurred.As used herein, contingent information (or condition) may includeinformation (or condition) that is not controlled by, e.g., a servicerequester, a service provider, etc., or information (or condition) thatis temporary. For example, contingent information (or condition) mayinclude a weather condition, an environmental condition, a roadcondition (e.g., a road is closed for roadwork or security reasons), atraffic condition, or the like, or a combination thereof. The historicalinformation or the contingent information (or condition) may relate toan order, e.g., the origin of the order, the destination of the order,along a route between the origin and the destination of the order, orthe like. In another embodiment, the received information includes,e.g., an order location, a provider location, a time when an order isplaced, an order acceptance rate, a traffic condition, a road condition,a weather condition, historic information, or the like, or a combinationthereof. At block 302, a locus is marked based on the receivedinformation. As described above, this may be performed by anidentification module 220 in the scheduling system 102. The marking maybe based on information regarding a certain area or region including,e.g., the distribution of orders, the number of orders, the number ofservice providers, the weather condition, the road condition, thehistorical information, the traffic condition, or the like, or acombination thereof. At block 303, information regarding the markedlocus is delivered to, e.g., one or more requesters, one or moreproviders, or the like, or a combination thereof.

FIG. 3-b is a flowchart of another exemplary process in which ascheduling system 102 is deployed, according to an embodiment of thepresent teachings. Beginning at block 304, information is received. Thecollection of information may be performed by a collection module 210 inthe scheduling system 102. As described above, the received informationmay be information relating to orders, requesters, providers, historicalinformation, contingent information, or the like, or a combinationthereof. In one embodiment, at least some of the received information isreal-time information. At block 305, a locus is identified based on afirst set of information relating to a certain area or region. Anidentified locus may be described that orders sharing at least a samecharacteristic are grouped together according to an embodiment ofpresent teachings. As described above, this may be performed by theidentification module 220 in the scheduling system 102. The first set ofinformation may be a subset of the information received at thecollection module 210. The first set of information may include, but benot limited to, information regarding a certain area or regionincluding, e.g., the starting locations (origins) of orders, thedestinations of orders, the distribution of orders, the number oforders, the number of service providers, the weather condition, the roadcondition, historical information, the traffic condition, or the like,or a combination thereof. At block 306, the identified locus is markedbased on a second set of information. This may also be performed by theidentification module 220. The second set of information may relate tothe identified locus. The second set of information may overlap, atleast partially, with the first set of information. The second set ofinformation may be a subset of the information received at thecollection module 210. The second set of information may include, e.g.,the distribution of orders, a certain region or area, the number oforders in the identified locus, the number of service providers in theidentified locus, the weather condition, the road condition, thehistorical information relating to the identified locus, the trafficcondition relating to the identified locus, the order acceptance rate inthe identified locus, provider-requester ratio in the identified locus,or the like, or a combination thereof. In one embodiment, an identifiedlocus at block 305 may be directly marked or treated as being marked,then it proceeds to block 307 and determines to which service providersand/or requesters the information relating to the marked locus is to bedelivered. In one embodiment, the first set of information is the sameas the second set of information. In one embodiment, the criterion (orcriteria) under which a locus is identified is the same as the criterion(or criteria) under which a locus is marked. An identified locus ismarked. In one embodiment, at block 306 the scheduling system 102determines whether the locus is balanced, under-served, or over-served.Information relating to the marked locus at 306 may be forwarded toblock 304, where the information initially received by the collectionmodule 210 may be updated, and/or the identification module 220 maymodify or update the information relating to the marked locus. At block307, a determination is made regarding to which service providers orrequesters the information relating to the marked locus is to bedelivered. At block 308, the information relating to the marked locus isdelivered.

In some embodiments, the information relating to an identified or markedlocus may be delivered to one or more service providers, and/or one ormore service requesters, or one or more third party, as illustrated inFIG. 3-a and FIG. 3-b. The information delivered to a service providermay be the same as that delivered to a service requester. Merely by wayof example, the information delivered to a service provider and aservice requester includes where the locus is, the estimated time for aservice provider to reach a service requester or the locus, the locationof a service provider or a service requester, the road condition, theweather condition, or the like, or a combination thereof. Theinformation delivered to a service provider may be different from thatdelivered to a service requester. Merely by way of example, theinformation delivered to a service provider includes where the locus is,the location of one or more service requesters, and information relatingto one or more orders of the locus (e.g., the origin, the destination,the number of passengers, the number of luggage pieces, whether a tip isoffered, etc.); the information delivered to a service requesterincludes whether an adjacent area has more service providers, how longthe estimate waiting time is, the weather condition, the road condition,the location of one or more service providers, or the like, or acombination thereof.

While the foregoing has described what are considered to constitute thepresent teachings and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. Those skilled in the art will recognize thatpresent teachings are amenable to a variety of modifications and/orenhancements.

FIG. 4 is a block diagram illustrating an architecture of the collectionmodule 210 according to an embodiment of present teachings. Thecollection module 210 includes a historical information unit 410, anorder information unit 420, a provider information unit 430, and acontingent information unit 440. It is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. Those skilled in the art will recognize thatpresent teachings are amenable to a variety of modifications and/orenhancements. For example, some of the described modules or units maybeembodied in a single module or unit, or a single module or unit mayconduct the functions of two or more of the mentioned modules or units.

The historical information unit 410 may be configured to receivehistorical information from, e.g., the user log DB 106 and/or from atleast one third party (e.g., service center, etc.). In an embodiment,the historical information unit 410 further includes a historicalinformation DB 250 used for storing and/or processing historicalinformation. As described, historical (or historic) information mayinclude past information relating to, e.g., the demand or supply of aservice (including same or similar services) in an area or region and/orover a period of time. The historical information may include, but isnot limited to, past and/or recent information, such as the number ofrequesters in an area or region at a certain time or over a period oftime, the time an order was placed, the number of orders, the locationsand/or times for pickup by taxis, the extra expense or tip a servicerequester was willing to pay, special conditions requested in an order(e.g., a lot of luggage, a lot of passengers, a specific type ofvehicle, etc.), the requesters' information stored in the user log DB106 and/or historical information DB 250, the gender, age, driving yearsor experience of a provider, vehicle age, vehicle type, license platenumber, the extra service capacity (e.g., extra features of thevehicle), order number, number of accepted orders, order acceptancerate, requesters' habits, the taxis' location and so on. The historicalinformation may be collected and stored in one or more databases, suchas through cloud data storage or locally on a server or computer. Thehistorical information may also come from at least one entity ororganization which is, but is not limited to, a commission by governmentand/or enterprises.

The order information unit 420 may be configured to receive one or moreorders from, e.g., requesters and/or from a third party via, e.g., anapplication or a portal (e.g., a terminal that is configured tocommunicate with, by way of sending information to and/or receivinginformation from, the scheduling system via a network). Such anapplication or portal may be installed on a device, e.g., a smart phone,a desktop, a laptop, or a device described elsewhere in the presentteachings or known to those of ordinary skill in the art. Merely by wayof example, a third party may make an order for a service on behalf of apassenger or a group of passengers using such an application. The ordermay include information regarding, e.g., the time an order is placed,the number of taxis, the location for pickup (or origin), thedestination, the time for pickup, the contact information, the number ofpassengers, the number of luggage pieces, the tip that requesters arewilling to pay, additional conditions requested relating to the order,whether a driver is needed or not (e.g., the service requester willdrive himself or has a driver), or the like, or a combination thereof.

The provider information unit 430 may be configured to receive providerinformation from, e.g., providers and/or from a third party via, e.g.,an application or a portal as described above. The provider informationmay include, but is not limited to, information specific to a providerand/or a taxi, such as gender, age, driving years or experience of aprovider, the number of accepted orders, the order acceptance rate atspecific times or over periods of time, the vehicle age, the vehicletype, the capacity of the vehicle, the license plate number, the taxi'slocation, extra service capacity (e.g., extra features of the vehicle),whether the vehicle is available for use without providing a driver(e.g., the service requester himself will have to drive the vehicle orarrange a driver), or the like, or a combination thereof.

The contingent information unit 440 may be configured to receivecontingent information from one or more sources, including, e.g.,official news systems (e.g., a weather report system, a real time roadconditions system, a broadcast station, etc.) and/or from at least onethird party via, e.g., an interface, a portal, an application (e.g., 3Drealistic scene by Google map, etc.), or the like, or a combinationthereof. In an embodiment, the contingent information includes acontingent information source 260. The contingent information includes,but is not limited to, the information from the contingent informationsource 260, such as a traffic condition relating to an order or an orderlocus, the road condition relating to an order or an order locus, theweather condition relating to an order or an order locus, or the like,or a combination thereof. For example, when the weather is rainy, thecontingent information unit 440 may receive the “Rainy” information fromthe contingent information source 260 that may be connected to, e.g., areal time weather forecast system, then the information may be processedor forwarded to another portion of the scheduling system 102, e.g., tothe identification module 220.

FIG. 5 is a block diagram illustrating an architecture of theorder/provider information unit 420/430 according to an embodiment ofpresent teachings. The order/provider information unit 420/430 includesa location information collector 510 and a miscellaneous informationcollector 520. It is understood that various modifications may be madethereto and that the subject matter disclosed herein may be implementedin various forms and examples, and that the teachings may be applied innumerous applications, only some of which have been described herein.Those skilled in the art will recognize that present teachings areamenable to a variety of modifications and/or enhancements. For example,the collectors described above maybe embodied in a single collector, ora collector may conduct the functions of both collectors.

The location information collector 510 may be configured to collectlocation information from, e.g., a requester or a device associated withthe requester, a provider or a device associated with the provider, athird party or a device associated with the third party, or the like.For example, the location information may include the location forpickup, the destination to go, etc.

The miscellaneous information collector 520 may be configured to collectinformation from requesters and/or from at least one third partyaccessible application. Miscellaneous information may includeinformation relating to an order (e.g., the time constraint, the numberof passengers, the number of luggage pieces, the size of luggage, thelocation and/or the time of the pickup, the destination, the amount oftip the requester is willing to pay, a passenger's habits orpreferences, or the like, or a combination thereof), informationrelating to a provider (e.g., gender, age, driving years or experience,the vehicle age, the vehicle type, the license plate number, extraservice capacity (e.g., extra features of the vehicle), the ordernumber, the number of accepted orders, the order acceptance rate atspecific times or over periods of time, the taxis' locations, whetherthe vehicle is available for self-driving), other input information froma passenger, a provider, or a third party, or the like, or a combinationthereof. Miscellaneous information may also include contingentinformation relating to an order or a locus.

FIG. 6 is a block diagram illustrating an architecture of the locationinformation collector 510 according to an embodiment of the presentteachings. The location information collector 510 may include a receiver610 and a location information processor 620. It is understood thatvarious modifications may be made thereto and that the subject matterdisclosed herein may be implemented in various forms and examples, andthat the teachings may be applied in numerous applications, only some ofwhich have been described herein. Those skilled in the art willrecognize that present teachings are amenable to a variety ofmodifications and/or enhancements. For example, the collectors describedabove maybe embodied in a single collector, or a collector may conductthe functions of both collectors.

In an embodiment, the receiver 610 may be configured to communicate withone or more positioning devices for receiving location information orlocation signal. A position device may be, e.g., a smart phone, a globalpositioning system, a desktop, a laptop, a tablet computer, anin-vehicle computing platform, a cloud computing based portable userplatform with location determined services, a personal digital assistant(PDA), a netbook, an ultrabook, a digital photo frame, a media player, ahandled gaming console, an ebook reader (e.g., Amazon kindle voyage,etc.), a global navigation satellite system (GLONASS), a Beidounavigation system (BDNS), a Galilio positioning system, a quasi-zenithsatellite system (QZSS), a base station (BS), a wearable computingdevice (e.g., eyeglasses, wrist watch, etc.), a virtual display device,a display enhanced device, a car PC, a car navigation, a radarchronograph, a laser velocimeter, or the like, or a combination thereof.A positioning device may emit or receive a positioning signal that maybe used to determine the location of the positioning device or a user ofthe device. The location information processor 620 may be configured toreceive the input regarding location information or identify thelocation of the received information, such that the geographic orlocation information (e.g., longitude, latitude, altitude, address, orthe like, or a combination thereof) of a requester, a provider, or thelike, may be determined. The input regarding location informationincludes, but is not limited to, location information from a requester,a provider, and/or at least one third party. For example, a requesterinputs a location where his/her friend, a passenger, needs to be pickedup for a taxi ride, when the passenger doesn't have a device with thepositioning function. It is understood that various modifications may bemade thereto and that the subject matter disclosed herein may beimplemented in various forms and examples, and that the teachings may beapplied in numerous applications, only some of which have been describedherein. Those skilled in the art will recognize that present teachingsare amenable to a variety of modifications and/or enhancements. As usedherein, a “taxi” is intended to refer to any means of transportationused to convey passengers or items in return for payment or fare,including but not limited to street taxis that pick up passengers on thestreet, livery vehicles that respond to prearranged trips, limousines,and delivery services.

FIG. 7 is a diagram illustrating a collection module 210 configured forreceiving information from various sources or devices according to anembodiment of the present teachings. The collection module 210 mayinclude at least one communication unit that may be configured toreceive information and/or one or more databases storing and/orprocessing related information. The collection module 210 maycommunicate with one or more positioning devices to receive the relatedinformation via the network 114, and/or transmit the receivedinformation to other portions of the scheduling system 102, e.g., theidentification module 220. The positioning device may include, e.g., amobile device with the positioning function, a vehicle having at leastone positioning module integrated and other instruments to detect thevelocity parameter, for example, a smart phone, a personal digitalassistant (PDA), a tablet, a laptop, a netbook, a desktop, an in-vehiclecomputing platform, a cloud computing based portable user platform withlocation determined services, a personal digital assistant (PDA), anetbook, an ultrabook, a digital photo frame, a media player, a handledgaming console, an ebook reader (e.g., Amazon kindle voyage, etc.), aglobal positioning system (GPS), a global navigation satellite system(GLONASS), a Beidou navigation system (BDNS), a Galilio positioningsystem, a quasi-zenith satellite system (QZSS), a base station (BS), awearable computing device (e.g., eyeglasses, wrist watch, etc.), avirtual display device, a display enhanced device, a car PC, a carnavigation, a radar chronograph, a laser velocimeter, or the like, orany combination thereof. A variety of wireless Internet technologies maybe used in the network 114, for example, Wireless LAN (WNAN) (Wi-Fi),Wireless broadband (WiBro), World Interoperability for Microwave Access(WiMax), High Speed Downlink Packet Access (HSDPA), and so on. A varietyof short range communication technologies may also be used in thenetwork 114, for example, Bluetooth (e.g., iBeacon, etc.), RadioFrequency Identification (RFID), Infrared Data Association (IrDA), UltraWideband (UWB), ZigBee, and so on.

FIG. 8 is a block diagram of the identification module 220 according toan embodiment of the present teachings. The structure and the componentsof the module in FIG. 8 may be applicable in the context of theidentification module 220, and also in the context of the determinationmodule 230. The following description is provided in the context of theidentification module 220 for illustration purposes, and is not intendedto limit the scope of the present teachings.

In one embodiment, the identification module 220 may be configured toreceive information from the collection module 210 and outputcalculation results. The information from the collection module 220 mayinclude but without limiting to historical information, orderinformation, provider information, contingent information, or the like,or a combination thereof. As shown in FIG. 8, the identification module220 may include a module calculation control unit 802, a modulecalculation configurations 804, a historical information processor 806,an order information processor 808, a provider information processor810, a contingent information processor 812, and a module integrationcontroller 814.

The module calculation control unit 802 may be configured to communicatewith the module calculation configurations 804, to receive informationfrom, e.g., the collection module 210, and send the received informationfor further process in one or more of the historical informationprocessor 806, the order information processor 808, the providerinformation processor 810, and the contingent information processor 812,or the like, or a combination thereof. The module calculation controlunit 802 may control the mode of calculation to be performed, accordingto instructions retrieved from the module calculation configurations804. The module calculation configurations 804 may include theinstructions regarding calculation to be performed in module calculationcontrol unit 802, historical information processor 806, orderinformation processor 808, provider information processor 810,contingent information processor 812, and module integration controller814. Merely by way of example, instructions retrieved from the modulecalculation configurations 804 may determine whether any one of thehistorical information processor 806, the order information processor808, the provider information processor 810, and the contingentinformation processor 812 is involved in a calculation; the calculationsequence between the historical information processor 806, orderinformation processor 808, and provider information processor 810; analgorithm in any one of the historical information processor 806, orderinformation processor 808, provider information processor 810, andcontingent information processor 812 to be used; the algorithm-relatedparameters in any one of the historical information processor 806, orderinformation processor 808, provider information processor 810, andcontingent information processor 812; how the intermediate results fromany one of the historical information processor 806, the orderinformation processor 808, the provider information processor 810, andthe contingent information processor 812 are to be integrated, or thelike, or a combination thereof. The instructions may be retrieved fromthe module calculation configurations 804 by the module calculationcontrol unit 802 based on, e.g., the information received by thecollection module 210, including information regarding an order orlocus, a provider, a contingent condition, historical information, aninstruction regarding the calculation to be performed or algorithm to beused provided by a requester, a provider, a third party, orautomatically selected by the system. Merely by way of example, if nohistorical information is received in connection with a locus, thehistorical information processer is bypassed in the calculation. Asanother example, if a requester specifies that a criterion in connectionwith an order (e.g., a time constraint, a tip to be provided, etc.), aspecific algorithm may be retrieved from the module calculationconfigurations 804 by the module calculation control unit 802 and usedto process relevant information. As another example, if a demand/supplyis determined based on a historic number of orders in a certain regionover a period of time and real time information of the number ofproviders in the region, the historical information processor andprovider information processor both may be involved in processing theinformation.

The historical information processor 806 may be configured to processhistorical information. The order information processor 808 may beconfigured to process order information relating to an order or a locus.The provider information processor 810 may be configured to processinformation relating to a provider. The contingent information processor812 may be configured to process contingent information relating to anorder or a locus. The historical information processor 806, the orderinformation processor 808, the provider information processor 810, andthe contingent information processor 812 each may be an independentcomputing unit. In another example, at least two of the historicalinformation processor 806, the order information processor 808, theprovider information processor 810, and the contingent informationprocessor 812 may share a computing unit with another.

FIG. 9 depicts an exemplary diagram of an order information processoraccording to an embodiment of the present teachings. The structure ofthe order information processor in FIG. 9 may be applicable in thecontext of the provider information processor 810. The order informationprocessor 808 may include an information processing control unit 902.The information process processing control unit 902 may be configured toreceive information to be processed, instructions from the informationprocessing configurations 904, or the like, or a combination thereof.The information processing control unit 902 may be configured to controlthe mode of calculation to be performed, according to instructionsretrieved from the information processing configurations 904. Theinformation processing configurations 904 may include the instructionsregarding calculation to be performed in various calculators andprocessing units of the order information processor 808 including, e.g.,the location information processor 906, the distance calculator 908, thetime calculator 910, and the miscellaneous information unit 912. Thelocation information processor 906 may be configured to process locationinformation, including, but is not limited to, location information fromrequesters and/or at least one third party. The location information mayalso include information relating to the starting location (origin), andthe destination location (or destination) of an order. The distancecalculator 908 may be configured to calculate the distance between twolocations. The time calculator 910 may be configured to estimate a timefor a service provider/receiver to travel from one location to anotherbased on the distance information, e.g., that calculated by the distancecalculator 908. The miscellaneous information unit 912 may be configuredto process miscellaneous information from a requester, a provider, athird party, the contingent information, or the like, as describedabove. The information controller 914 may be configured to integrateinformation processed by the location information processor 906, thedistance calculator 908, the time calculator 910, the miscellaneousinformation unit 912, or the like, or a combination thereof, to outputprocessed order information. Merely by way of example, theidentification module 220 may identify or mark a locus based on trafficinformation and location information. Thus, the order informationprocessor 808 may configure the information processing control unit 902by information processing configurations 904 to access locationinformation processor 906 and miscellaneous information unit 912, andinformation integration controller 914 may process the integratedinformation and output the result to following module or unit.

Returning to FIG. 8, the historical information processor 806, the orderinformation processor 808, the provider information processor 810, andthe contingent information processor 812 may process respectiveinformation individually or cooperatively. The four mentioned processorsmay send respective processed data (or intermediate results) to themodule integration controller 814. The four processor modules sendinformation at the same time or at a predetermined sequence.

The module integration controller 814 may be configured to integrate thereceived or processed data (or intermediate results) and calculate aresult based on, the instructions retrieved from the module calculationconfigurations 804 by the module calculation control unit 802. Themodule integration controller 814 may be an independent computing unitor a shared computing unit with historical information processor 806,order information processor 808, provider information processor 810, andcontingent information processor 812.

The connection type between module calculation control unit 802, modulecalculation configurations 804, historical information processor 806,order information processor 808, provider information processor 810,contingent information processor 812, and module integration controller814 may be wired or wireless, all integrated in a circuit or partiallyintegrated in a circuit or distributed in different places.

The identification module 220 may process a plurality of orders, and mayidentify or mark at least one locus based on various information. Thevarious information may include information relating to orders,requesters, passengers, providers, contingent information, historicalinformation, a certain region or area, or geographic information of acertain region or area, or the like, or a combination thereof. Forexample, the identification of a locus or region may be based on orderdistribution by clustering algorithms, or based on region conditions. Alocus may include an order set including one or more orders. The ordersin the locus are more similar to each other than to those outside of thelocus. The orders in the locus share at least one characteristic. Merelyby way of example, a shared characteristic may be that a distancebetween a location relating to the locus (e.g., a reference point or alocation within the locus) and a location relating to an order of isless than a threshold. The threshold may be a constant, or a variable.Merely by way of example, the threshold may vary based on, e.g., thetime of the day, the day of the week, the road condition, the trafficcondition, a specific condition specified by a requester or a provider,or the like, or a combination thereof. The threshold may be apredetermined constant or a predetermined variable. For instance, thethreshold may be a variable as a function of time, a function of acontingent condition, or a function of two or more parameters, or thelike. The function may be derived from, e.g., historical informationusing a machine-learning algorithm. Exemplary machine learning algorithmis described elsewhere in the present teachings.

FIG. 10-a and 10-b illustrate the exemplary diagrams of identifyingregions on the basis of which loci may be identified or marked,according to one embodiment of the present teachings. FIG. 10-a is anexample of how the orders are grouped or organized into different loci(one elliptical area indicating one locus), and the dots that do notfall within any elliptical area do not belong to any locus. A dotindicates a location relating to an order for a service, e.g., thestarting location or origin of an order for a taxi ride.

FIG. 10-b is another example of identification of a certain area basedon geographic information. As illustrated in FIG. 10-b, an area (e.g., acity) is divided into 30 m*30 m grids. Let r be an order request fromcollection module, whose longitude and latitude coordinates are(longitude, latitude). Let the longitude and latitude coordinates of thelower left corner of the map be (leftLongitude, leftLatitude), the widthof a gird be width, thus the grid number (gridCx, gridCy) maybecalculated by:

gridCx=(int)((longitude−leftLongitude)/width)

gridCy=(int)((latitude−leftLatitude)/width)

It shall be noted that the identification of loci or regions shall notbe restricted by the examples described above since they are simplyspecific embodiments of the present teachings. Those having ordinaryskills in the art will recognize that the present teachings areamendable to a variety of modifications and/or enhancements. Forexample, the division of regions in FIG. 10-b may be amended accordingto clustering algorithms as described in FIG. 10-a, the width of thegrids may be variable according to specific algorithms.

FIG. 11 is a diagram illustrating how a locus/region partition algorithmin the identification module input and output according to the presentteachings. Locus/region partition algorithm may be utilized to identifya locus or region based on information which is input and relating to atleast one piece of information selected from, e.g., historicalinformation, order information, provider information, contingentinformation, or the like, or a combination thereof, as describedelsewhere in the present teachings.

Information used in the identification module 220 may be stored in oneor more storage devices (not shown in figures) inside the schedulingsystem 102 or outside of the scheduling system 102 (e.g., in a storageprovided by a vendor). In some embodiments, the identification module220 may be configured to receive, e.g., historical information, orderinformation, provider information, contingent information, or the like,or a combination thereof. Such information may be from the collectionmodule 210. The identification module 220 may be configured to identifyat least one locus based on the received information and an algorithm.In an embodiment, the region partition algorithm may partition an area(e.g., a city, a district, etc.) into at least one region according to,but not limited to, longitude and latitude, coordinate, position, sizeof the area, density, and/or grid, as illustrated in FIG. 10-a and FIG.10-b.

FIG. 12 is another diagram illustrating an exemplary region partitionalgorithm in the identification module input and output according to thepresent teachings. The algorithm illustrated in FIG. 12 is a clusteringalgorithm. An applicable clustering algorithm may be CLARANS, PAM,CLATIN, CLARA, DBSCAN, BIRCH, OPTICS, WaveCluster, CURE, CLIQUE, K-meansalgorithm, hierarchical algorithm, or the like, or a combinationthereof. In one embodiment, the Dbscan clustering algorithm may define adistance and automatically put all the orders under one certain orderlocus based on the latitude and longitude of starting locationinformation of all the orders. The orders in a locus may share a samecharacteristics. For example, each order in a locus is located within acertain distance from another order. As shown in FIG. 12, inputs ofDbscan clustering algorithm include, e.g., order set D, radius r, andparameter e. The order set D is the set of all the orders in apredetermined time period, radius r is to define an order's r region,and density threshold e is the minimum number of orders for a specificorder to be determined or defined as a core order in the r region. Basedon received inputs order set D, the order set D, and specifiedparameters (radius r and density threshold e), the identification moduleoutputs at least one identified locus (or referred to as order locus).According to an embodiment of present teachings, the Dbscan clusteringalgorithm is applied to calculate at least one order locus in a timeperiod based on the order set D of the time period. The order set D mayinclude: the respective order numbers, the respective starting locations(by way of, e.g., latitude and longitude of starting location), therespective starting times, or the like, or a combination thereof. Thespecified radius r and density threshold e utilized in the clusteringalgorithm may be modified depending on different conditions, such ashistorical information, information provided by one or more requesters,or contingent information.

FIG. 13 is a flowchart diagram of an exemplary application of Dbscanclustering algorithm according to an embodiment of the presentteachings. A vehicle order set D is the set of orders for vehicleservices within a city in a time period (e.g., a predetermined timeperiod), and the information in an order of the order set includes:order number (ID), latitude and longitude of starting location, startingtime and etc.; radius r is determined by experience, generally 1-5 km;density threshold e is generally 1/10˜ 1/50 of all the vehicle orders incurrent city; each locus C is one of the order loci in one certain cityat current moment. The time period may be 5 minutes, 10 minutes, 15minutes, 20 minutes, 25 minutes or 30 minutes; the refresh time ofinformation is 5 seconds, 10 seconds, 15 seconds, 20 seconds, 25 secondsor 30 seconds.

Merely by way of example, the Dbscan clustering algorithm includes thefollowing steps:

Step 1, at block 1301, detecting order p in order set D; at decisionblock 1302, determining whether the order p in order set D is processed.If order p is grouped under one certain locus or is marked as noise,which means p is processed, then returning to block 1301, detecting thenext order in D. If order p is not processed, then proceeding to block1303, adding all the orders in order p's r region (if the distancebetween the latitude and longitude of starting location of one certainorder and the latitude and longitude of starting location of order p isless than radius r, the order is considered as an order in order p's rregion) to candidate set N.

Step 2, at block 1304, detecting the number of orders in candidate setN, at decision block 1305, determining whether the number of orders oforder set N is less than e. If the number of orders in order set N isless than e, proceeding to block 1306, marking order p as noise, thenreturning to block 1301, and detecting the next order in D. If thenumber of orders in order set N is equal to or more than e, thenproceeding to block 1307, creating a new locus C and adding order p tolocus C.

Step 3, at block 1308, detecting order p′ in candidate set N, atdecision block 1309, determining whether the order p in N is processed.If order p′ is grouped under one certain locus or is marked as noise,which means p′ is processed, then returning to block 1308, detecting thenext order in N. If order p′ is not processed, then proceeding to block1310, adding all the orders in order p's r region to candidate set N′.At block 1311, detecting the number of orders of order set N′; atdecision block 1312, determining whether the number of orders of orderset N′ is less than e. If the number of orders of order set N′ is equalto or more than e, then proceeding to block 1313, adding orders N′ to N;at decision block 1314, determining whether p is grouped under anylocus. If order p′ is not grouped under any locus, then proceeding toblock 1315, adding order p′ to locus C.

Step 4, at decision block 1316, determining whether order set N is alldetected. If N is not all detected, then returning to block 1308,detecting next order in N, repeating step 3, until order set N is alldetected. If N is all detected, then proceeding to decision block 1317.

Step 5, at decision block 1318, determining whether order set D is alldetected. If D is not all detected, then returning to block 1301,detecting next order in D, repeating step 1-3, until order set D is alldetected. If D is all detected, then proceeding to END.

Thus, after Dbscan algorithm clustering, outputting a plurality of loci,each of which may include orders relating to a certain location.

Dbscan clustering algorithm pseudo-code may be described as follows:

Dbscan (D,r,e) Begin Init C = 0; For each unvisited point p in D Mark pas visited; N = getNeighbours (p,r); If sizeOf(N) < e then Mark p asNoise; else C = next cluster; ExpandCluster (p, N, C, r, e); end if endfor End

The ExpandCluster algorithm pseudo-code may be described as follows:

ExpandCluster(p, N, C, r, e) add p to cluster C; for each unvisitedpoint p′ in N mark p′ as visited;  N′ = getNeighbours (p′, r); ifsizeOf(N′) >=e then N = N+N′;  end if if p′ is not member of any clusteradd p′ to cluster C; end if end for End ExpandCluster

Furthermore, based on the orders in a locus, parameters including, e.g.,the locus center, the radius, the number of providers, the number oforders in the locus, or the like, or a combination thereof, may becalculated.

Merely by way of example, based on the latitude and longitude of all theorders in the locus, the latitude and longitude of the locus center maybe calculated using mean value. After obtaining latitude and longitudeof the locus center, the distance between the latitude and longitude ofthe locus center and latitude and longitude of each order in the locusmay be calculated, and the maximum value of the distance maybe taken asthe radius of the region. The number of orders in the region is thetotal number of all the orders in the locus. Number of providers in theregion is determined as: calculating the distance between latitude andlongitude of the locus center and each providers, and counting thenumber of the providers whose distance are less than the radius of theregion.

Thus, one or more order loci may be identified in a certain area (e.g.,in a certain city), for example, locus 1 is described with centercoordinate xy (latitude and longitude of the locus center), radius r,number of orders n, number of providers m; locus 2 is described bycenter coordinate xy′, radius r′, number of orders n′, number ofproviders m′, etc.

Based on the order information in one certain order locus, theidentification module 220 may be configured to calculate, e.g., thenumber of orders in the order locus, the location (by way of, e.g.,latitude and longitude) of the locus center, radius of the locus, or thelike, or a combination thereof. As used herein, the number of orders inthe locus is the total number of orders in the order locus; the latitudeand longitude of the locus center is the average value of all thelatitude and longitude of the locus; and the radius of the locus is themaximum value of the distance between the latitude and longitude of thelocus center and latitude and longitude of starting location of eachorder in the order locus. There are other ways to define the location ofthe locus center. The description provided above is for illustrationpurposes, and is not intended to limit the scope of the presentteachings. The orders in the locus may be closer to each other thanorders outside of the locus. A locus so identified may have aconcentration of orders for a service (or the same or similar services).

In one embodiment, the identification module 220 may be configured tofurther mark an identified locus if the locus meets a certain criterionindicating a mismatch of the demand for service within the identifiedlocus and the supply of the service within the identified locus. Thedetermination may be made based on, e.g., the information received fromthe collection module 210, or information processed by the historicalinformation processor 806, the order information processor 808, providerinformation processor 810, and/or contingent information processor 812.The information may include historical information, order information,provider information, or contingent information, or the like, or acombination thereof. For example, the information may include thatrelating to the identified locus, including e.g., an order location, aprovider location, an order acceptance rate, an order acceptance rate, atraffic condition, a road condition, a weather condition, and historicalinformation, or the like, or a combination thereof. In an embodiment, aprovider-requester ratio and the order acceptance rate relating to theidentified locus are calculated. In one example, an identified locusthat meets a certain criterion is further marked as an “under-served”locus, where more service providers are needed. In another example, aprovider-requester ratio, a factor relating to the road condition, afactor relating to the traffic condition are calculated. Based on thecalculated parameter(s), an identified locus that meets a criterion isfurther marked as “under-served” locus.

FIG. 14 is a flowchart for marking the locus as whether under-served ornot according to an embodiment of the present teachings. The marking maybe performed in the identification module 220. Beginning at block 1402,information relating to a locus is received. At block 1404, a firstparameter is calculated based on the received information. The firstparameter may be a number (e.g., the number of orders within a locus), aratio (e.g., the order acceptance rate, a ratio of the number ofproviders in a locus to the number of orders in the locus(provider-requester ratio), etc.), or a contingent factor (e.g., afactor relating to the weather condition, a factor relating to thetraffic condition, etc.), or the like, or a combination thereof. Atblock 1406, the first parameter in a locus compares to a first thresholdto determine whether the locus is marked. Merely by way of example, ifthe first parameter is the order acceptance rate, then the threshold Amay be set at 80%, the locus is not further marked if the orderacceptance rate exceeds the threshold (i.e. the locus is in goodcondition as is). If the parameter is lower than the first threshold,then the identification module 220 proceeds to block 1408. The firstthreshold may be a constant, or a variable. Merely by way of example,the first threshold may vary based on, e.g., the time of the day, theday of the week, the road condition, the traffic condition, a specificcondition specified by a requester or a provider, or the like, or acombination thereof. The first threshold may be a predetermined constantor a predetermined variable. For instance, the first threshold may be avariable as a function of time, a function of a contingent condition, ora function of two or more parameters, or the like. The function may bederived from, e.g., historical information using a machine-learningalgorithm. Exemplary machine learning algorithm is described elsewherein the present teachings. At block 1408, a second parameter iscalculated. The second parameter may also be a ratio (e.g., the orderacceptance rate, the ratio of the number of providers within the locusto the number of orders within the locus, etc.), or a contingent factor(e.g., a factor relating to the weather condition, a factor relating tothe traffic condition, etc.). The second parameter may be different fromthe first parameter. At block 1410, the second parameter is compared toa second threshold. Merely by way of example, for a locus whose firstparameter is less than the first threshold A (e.g., the order acceptancerate is lower than the first threshold A set to be 80%), at block 1410,if the provider-requester ratio is below the second threshold B (e.g.,the provider-requester ratio is lower than the second threshold B set tobe 10), to the locus is marked at block 1412 indicating that the supplyof service is insufficient to satisfy the demand for service in thelocus. The locus is marked such that the information regarding themismatch of the demand and supply in the locus is to be delivered. Thesecond threshold may be a constant, or a variable. Merely by way ofexample, the second threshold may vary based on, e.g., the time of theday, the day of the week, the road condition, the traffic condition, aspecific condition specified by a requester or a provider, or the like,or a combination thereof. The second threshold may be a predeterminedconstant or a predetermined variable. For instance, the second thresholdmay be a variable as a function of time, a function of a contingentcondition, or a function of two or more parameters, or the like. Thefunction may be derived from, e.g., historical information using amachine-learning algorithm. Exemplary machine learning algorithm isdescribed elsewhere in the present teachings.

Those having ordinary skills in the art will recognize that the presentteachings are amendable to a variety of modifications and/orenhancements. For example, at least one parameter is necessary in themodule to determine whether a locus shall be further marked. In someembodiments, more than two parameters shall also be implementable tofurther mark a locus.

The determination module 230 may be configured to determine to whom theinformation relating to a marked locus is to be delivered. Asillustrated in FIG. 8, the determination module 230 may be similar tothe identification module 220 described above. In one embodiment, thedetermination module, or a portion of the determination module (e.g., adistance calculator of the provider information processor of thedetermination module) may be configured to calculate a distance betweenthe first location relating to a marked locus and a location relating toa provider. The distance may provide the basis to determine to whom theinformation relating to a marked locus is delivered. Merely by way ofexample, the information relating to a marked locus is delivered to aprovider if the distance between the location of the provider and thefirst location (e.g., the location relating to the marked locus) is lessthan a threshold. In another embodiment, the determination module, or aportion of the determination module (e.g., a time calculator of theprovider information processor of the determination module) may beconfigured to calculate a time for at least one provider to travel tothe first location (e.g., the location relating to the marked locus). Tocalculate the time, the time calculator may use, e.g., the informationrelating to the location relating to the provider and the first location(e.g., the location relating to the marked locus) or the distancebetween the two locations, contingent information (e.g., the roadcondition, the traffic condition, various routes available between thetwo locations, etc.), the condition of the vehicle the provider isusing, or the like, or a combination thereof. The time may provide thebasis to determine to whom the information relating to a marked locus isdelivered. Merely by way of example, the information relating to amarked locus is delivered to a provider if the time is less than athreshold. The determination module may be configured to determinewhether to deliver the information relating to a locus to a requester.The threshold described above may be a constant, or a variable. Merelyby way of example, the threshold may vary based on, e.g., the time ofthe day, the day of the week, the road condition, the traffic condition,a specific condition specified by a requester or a provider, or thelike, or a combination thereof. The threshold may be a predeterminedconstant or a predetermined variable. For instance, the threshold may bea variable as a function of time, a function of a contingent condition,or a function of two or more parameters, or the like. The function maybe derived from, e.g., historical information using a machine-learningalgorithm. Exemplary machine learning algorithm is described elsewherein the present teachings.

FIG. 15 is a flowchart of an exemplary process of determining to whichservice provider(s) the information relating to a marked locus is to bedelivered, according to an embodiment of the present teachings.Beginning at block 1501, information relating to a locus is received.The locus may be a marked locus. In an embodiment, the informationrelating to the marked locus includes, but is not limited to, the locuscenter, the locus radius, the order acceptance rate, the origin of thedestination of an order, or the like, or a combination thereof. In oneembodiment, the information relating to the marked locus may furtherinclude historical information of the locus, a contingent condition(e.g., the traffic condition, the road condition, the weather condition,etc.), or the like, or a combination thereof. At block 1502, informationrelating to a service provider is received. In an embodiment, theprovider related information includes, but not limited to, position ofthe taxi. In an embodiment, the provider related information furtherincludes driver's ID, reporting time, the status of the taxi, or thelike, or a combination thereof. At block 1503, if the provider relatedinformation meets a criterion (or criteria) (i.e. the first condition asillustrated in FIG. 15), advertisement related to the marked locus isdelivered to the provider at block 1504. If the provider relatedinformation does not meet the criterion (or criteria), it may go to theend block or return to block 1502 to receive information of anotherservice provider. In an embodiment, the criterion (or criteria)correlate(s) with parameters including, e.g., the position of theprovider, the locus area, or the like, or a combination thereof. Forexample, the criterion (or criteria) may be that if the distance betweenthe provider and the nearest locus is within the interval (r′, r′+d),meaning the distance is equal to or more than r′, while equal to or lessthan r′+d. Here, r′ is the radius of the marked locus, d is apredetermined value. For instance, d may be set to be a value between0.5 km and 2 km. The position of the provider and distance between theprovider and the locus may be provided by, e.g., the distance calculator908, or by respective units or parts included in other modules describedabove.

In one embodiment, the delivery module may be configured to deliver theinformation relating to the marked locus to various recipientsincluding, e.g., providers around marked loci. At block 1504,information relating to the marked locus (e.g., in the form ofadvertisement or notification) is delivered to the providers thatsatisfy the first condition. According to an embodiment of the presentteachings, the advertisement to schedule or encourage providers from anoutside region to the marked locus may be realized by several methods orany combination of them. For example, information delivered to one ormore providers from a region outside of a locus may include at least onepiece of information selected from, e.g., the number of shortage inproviders in a locus in a period of time, a locus marked as over-servedor under-served, the distance between the provider's position and amarked locus, an estimated time for the provider to reach the markedlocus or an order within the locus, a route (e.g., a faster route, aroute without toll, etc.) that the provider may use to reach the markedlocus or an order within the locus, a suggested route, or the like, or acombination thereof. For inconvenient traffic area, traffic informationmay be delivered to the providers. Alternative information, such as roadadministration reconstructive area, may also be delivered to theproviders to indicate the road condition. In another example, theadvertisement may be a distribution diagram showing the densitydistribution of providers/passengers with different colors or whateverdistinguishable patterns. In another example, the advertisement may beaccompanied by changing the pricing standard for service in the markedlocus, and the pricing standard may also be sent to requesters so thatrequesters and providers may reach an agreement in advance. In oneembodiment, some information relating to the marked locus is deliveredto a requester within the locus. Merely by way of example, if anadjacent region has more providers than where the requester is, thisinformation may be delivered to the requester. The requester may alsoreceive information regarding a fast route to get to the adjacent area,the distance that the requester needs to travel, the road condition, thetraffic condition, or the like, or a combination thereof. Informationdelivered to one or more providers, one or more requesters, etc., may bedelivered in the format of, e.g., a text message, a voice message,graphic information that may be displayed on a screen, an animation, orthe like, or a combination thereof. The recipient of the information,e.g., a provider, a requester, etc., may specify the content of theinformation to be delivered, the format the information may bepresented, the device to which such information is to be delivered, orthe like, or a combination thereof.

Those skilled in the art will recognize that present teachings areamenable to a variety of modifications and/or enhancements. For example,the information of a service provider maybe received before theinformation relating to a marked locus being received, or the two set ofinformation maybe received simultaneously or essentially simultaneously.In another example, the information relating to a marked locus may bedelivered if two or more conditions are met. For example, a firstcondition may be if the distance between the provider and any locusregion center (or locus center) is more than the radius of the locus(indicating that the provider is not in any of the loci). A secondcondition may be if the distance between the provider and the nearestlocus is on the interval (R, R+D) (which means the distance is equal toor more than R, while equal to or less than R+D), wherein R is aparameter based on the radius of the loci, D is an variable orinvariable determined by the system.

In another embodiment according to the present teachings, the deliverymodule may deliver advertisement to service requesters. For example,provided that loci are marked to be advertised, in case of a contingenthappens, such as a traffic jam in a locus, a change in weathercondition, or an end of an activity in a locus, the system may deliveradvertisement or guidance information to requesters to conductrequesters in the specific locus to a different region that may beeasier to get a vehicle service. According to the embodiment of thepresent teachings, the traffic jam condition may be determined bysatellite views or vehicle density of the road monitor. Moreover, in thecase of a road closing for maintenance, advertisement or guidanceinformation may be delivered to requesters.

FIG. 16 is a flowchart of another exemplary process of deliveringadvertisement to service providers, according to an embodiment of thepresent teachings. At Block 1601, locus relating information isreceived. At Block 1602, information of service providers and requestersis received. As described above, the sequence of block 1601 and block1602 is exchangeable. At block 1603, determination is made regardingwhether a third condition is met. If the third condition is met, theoperation illustrated in block 1604 is performed; otherwise, it returnsto block 1602. At block 1604, information relating to a locus isdelivered to one or more providers, or one or more requesters. In anembodiment, the third condition is related to combining orders. Forexample, if an order belongs to a locus, and is connectable with anotherorder, then information of both orders may be delivered to the providerwho accepts the first order. An exemplary method for combining theorders includes the following operations: of two orders Oi and Ojcollected by the collection module, Oj is first order as it departsearlier, and Oi is second order as it departs later, and Oi belongs to alocus A. If the distance Dij between the departure point of second orderOi and the destination point of first order Oj is less than or equal toa distance threshold Dbase, then order Oi and order Oj are determined tobe connectable on distance; and if the time interval Tij between thedeparture time of second order Oi and the arriving time of first orderOj is less than or equal to a time threshold TBase, then order Oi andorder Oj are determined to be connectable in time. If order Oi and orderOj are determined to be connectable both on distance and in time; thenOi, Oj are determined to be possible combined orders. In an embodiment,Dbase is between 4 km to 6 km, TBase is between 5 minutes and 15minutes. In another embodiment, the distance is calculated by distancecalculator and the time interval is calculated by time calculator in thedelivery module. In an embodiment, the distance and time interval aredetermined based on order information including, but not limited to,order number, longitude and latitude of departure point, longitude andlatitude of destination point, and time of depart. It shall be notedthat the described order information may be collected by collectionmodule, or part of which is directly from requester's input.

FIG. 17 depicts the architecture of a mobile device which may be used torealize a specialized system implementing the present teachings. In thisexample, the user device on which information relating to an order forservice or other information from the scheduling system is presented andinteracted-with is a mobile device 1700, including, but is not limitedto, a smart phone, a tablet, a music player, a handled gaming console, aglobal positioning system (GPS) receiver, and a wearable computingdevice (e.g., eyeglasses, wrist watch, etc.), or in any other formfactor. The mobile device 1700 in this example includes one or morecentral processing units (CPUs) 1740, one or more graphic processingunits (GPUs) 1730, a display 1720, a memory 1760, a communicationplatform 1710, such as a wireless communication module, storage 1790,and one or more input/output (I/O) devices 1750. Any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 1700. As shown inFIG. 17, a mobile operating system 1770, e.g., iOS, Android, WindowsPhone, etc., and one or more applications 1780 may be loaded into thememory 1760 from the storage 1790 in order to be executed by the CPU1740. The applications 1780 may include a browser or any other suitablemobile apps for receiving and rendering information relating to an orderfor service or other information from the scheduling system on themobile device 1700. User interactions with the information stream may beachieved via the I/O devices 1750 and provided to the scheduling system102 and/or other components of system 100, e.g., via the network 114.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein (e.g., the scheduling system 102, and/or other components of thesystem 100 described with respect to FIGS. 1-16). The hardware elements,operating systems and programming languages of such computers areconventional in nature, and it is presumed that those skilled in the artare adequately familiar therewith to adapt those technologies to themanagement of the supply of service as described herein. A computer withuser interface elements may be used to implement a personal computer(PC) or other type of work station or terminal device, although acomputer may also act as a server if appropriately programmed. It isbelieved that those skilled in the art are familiar with the structure,programming and general operation of such computer equipment and as aresult the drawings should be self-explanatory.

FIG. 18 depicts the architecture of a computing device which can be usedto realize a specialized system implementing the present teaching. Sucha specialized system incorporating the present teaching has a functionalblock diagram illustration of a hardware platform which includes userinterface elements. The computer may be a general purpose computer or aspecial purpose computer. Both can be used to implement a specializedsystem for the present teaching. This computer 1800 may be used toimplement any component of the management of the supply of service asdescribed herein. For example, the scheduling system 102, etc., may beimplemented on a computer such as computer 1800, via its hardware,software program, firmware, or a combination thereof. Although only onesuch computer is shown, for convenience, the computer functions relatingto the management of the supply of service as described herein may beimplemented in a distributed fashion on a number of similar platforms,to distribute the processing load.

The computer 1800, for example, includes COM ports 1850 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 1800 also includes a central processing unit (CPU) 1820, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1810,program storage and data storage of different forms, e.g., disk 1870,read only memory (ROM) 1830, or random access memory (RAM) 1840, forvarious data files to be processed and/or communicated by the computer,as well as possibly program instructions to be executed by the CPU. Thecomputer 1800 also includes an I/O component 1860, supportinginput/output flows between the computer and other components thereinsuch as user interface elements 1880. The computer 1800 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of the management of supply of serviceand/or other processes, as outlined above, may be embodied inprogramming. Program aspects of the technology may be thought of as“products” or “articles of manufacture” typically in the form ofexecutable code and/or associated data that is carried on or embodied ina type of machine readable medium. Tangible non-transitory “storage”type media include any or all of the memory or other storage for thecomputers, processors, or the like, or associated modules thereof, suchas various semiconductor memories, tape drives, disk drives and thelike, which may provide storage at any time for the softwareprogramming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of a scheduling system into thehardware platform(s) of a computing environment or other systemimplementing a computing environment or similar functionalities inconnection with the management of supply of service. Thus, another typeof media that may bear the software elements includes optical,electrical and electromagnetic waves, such as used across physicalinterfaces between local devices, through wired and optical landlinenetworks and over various air-links. The physical elements that carrysuch waves, such as wired or wireless links, optical links or the like,also may be considered as media bearing the software. As used herein,unless restricted to tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

Hence, a machine-readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, which may be used to implement the system orany of its components as shown in the drawings. Volatile storage mediainclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution—e.g., an installation on an existing server. In addition,the scheduling system for management of supply of service as disclosedherein may be implemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

EXAMPLES

The following examples are provided for illustration purposes, and arenot intended to limit the scope of the present teachings.

Example 1

Using Beijing as an example, vehicle demands from a suburban area duringthe morning rush-hours are much more than those from an area around thecity center. For example, there is a great demand for taxi services inthe Huilongguan area from 8:00 am to 9:00 am; during the eveningrush-hour, for example form 18:00 pm to 19:00 pm, there is a greatdemand for taxi services in the Zhongguancun area.

At the server (or a scheduling system) of taxi booking software or callcenter, a great number of booking orders collected from passengers arestored. In general, the format of the booking orders collected frompassengers is as follows in Table 1:

TABLE 1 Longitude & Passenger Latitude of Phone Starting StartingStarting Order ID Number point Time location 140002 13300000001Zhongguancun 2014/2/20 xxxxxx Street No. 10 18:00 140012 13300000002Zhongguancun 2014/2/20 xxxxxx Street No. 20 18:00

Normally, once a passenger makes an order for a taxi, an orderinformation in an entry in Table 1 may be sent to the server.

The server (or a scheduling system), according to the starting locationof the orders, performs a statistical analysis (e.g., using the Dbscanclustering algorithm) on the orders in a certain area (e.g., Beijing)during a certain time period (from 18:00 pm to 18:05 pm in the sameday), and identifies a plurality of loci(region1: around Zhongguancunarea, 2.5 km radius, number of passengers: 200; region2: around Shangdi,3.4 km radius, number of passengers: 300; . . . ).

Each taxi reports its latitude and longitude information every 10seconds by an application the taxi driver uses. An exemplary format ofthe information is as follows in Table 2:

TABLE 2 Longitude & Driver ID Reporting time Current point Latitude12345 2014/2/20 Around Renmin University Xxxxxx 18:00 of China

After selecting appropriate locus, the server (or the scheduling system)may deliver information “Hello Mr., X km from you, a great number ofpassengers demand vehicle in XXX” to one or more taxi drivers(providers).

Example 2

A scheduling system identifies a plurality of loci utilizing Dbscanclustering algorithm based on the vehicle demands distribution ofShanghai. The scheduling system also calculates the number of orders,the location (by way of, e.g., the latitude and longitude) of the locuscenter, the locus radius, the number of providers, and the number oforders accepted, the order acceptance rate, the provider-requester ratioin a locus, based on, e.g., the order information and the providerinformation in the locus.

The order information may include: order numbers, starting locations (byway of, e.g., the latitude and longitude), starting times, whether anorder has been accepted, or the like, or a combination thereof; the taxiinformation may include: driver numbers (IDs), report times, thelocation (by way of, e.g., longitude and latitude) of providers. Thenumber of orders in the locus may be the total number of orders in thelocus. The latitude and longitude of the locus center may be the averagevalue of the latitude and longitude of all the orders in the locus. Theradius of the locus may be the maximum distance between the locus centerand of the location relating to an order in the locus. The number ofproviders in the locus may be the total number of the providers whosedistance between the locus center and the location of their taxi is lessthan the radius of the locus. The number of orders accepted may be thetotal number of orders accepted in the locus. The order acceptance ratemay be the ratio of the number of orders accepted and the number of allthe orders in the locus. The provider-requester ratio may be the ratioof the number of providers to the number of the orders in the locus.

In an exemplary scenario, the number of providers, the number of orders,order acceptance rate, provider-requester ratio are listed as follows inTable 3:

TABLE 3 Acceptance Provider-requester Number Number Number rate(numberof ratio(number of of of of acceptance/ providers/ orders acceptanceproviders number of orders) number of orders) 13 7 175 0.538462 13.4615423 12 228 0.521739 9.913043 20 14 199 0.7 9.95 16 7 120 0.4375 7.5 13 9155 0.692308 11.92308 22 7 140 0.318182 6.363636 33 12 299 0.3636369.060606 15 6 147 0.4 9.8 14 6 73 0.428571 5.214286 9 3 77 0.3333338.555556 7 1 34 0.142857 4.857143 31 11 275 0.354839 8.870968 5 2 69 0.413.8 7 5 126 0.714286 18 10 2 45 0.2 4.5 5 5 59 1 11.8 22 7 140 0.3181826.363636 33 12 299 0.363636 9.060606 31 11 275 0.354839 8.870968 17 7 670.411765 3.941176 25 15 134 0.6 12.6 12 5 72 0.416667 6 8 4 31 0.5 3.8758 5 109 0.625 13.625 4 1 88 0.25 22 6 3 48 0.5 8 4 2 34 0.5 8.5 17 7 670.416667 6 8 4 31 0.5 3.875

From the data, it may be concluded that as the increasing ofprovider-requester ratio, the order acceptance rate significantlyincreases. Referring to FIG. 19, vertical axis represents theprovider-requester ratio, horizontal axis represents the orderacceptance rate. For the expected order acceptance rate set to be 1, theideal provider-requester ratio may be 16, which means the orderacceptance rate and the provider-requester ratio may roughly meet acertain linear relationship. As illustrated in FIG. 19, the linearrelationship may be approximated by the relationship y=8.6155x+4.8676.

A mismatch of the supply-demand in the locus may be indicated by theorder acceptance rate and the provider-requester ratio.

In some embodiments, if the order acceptance rate in one certain locusis above 80%, the locus may be considered healthy (i.e. thedemand-supply relationship may be considered healthy in the locus); ifthe order acceptance rate in one certain locus is under 80%, the locusmay be considered unhealthy (i.e. the demand-supply relationship may beconsidered unhealthy in the locus). As to a locus whose order acceptancerate is under 80%, if the provider-requester ratio (divide the number ofproviders by the number of orders) is under 10, the provider-requesterratio may be considered low. One reason for the low order acceptancerate may be that the number of providers are insufficient to satisfyorders for service in the locus. It may be beneficial to encourageproviders from adjacent regions to enter and service the locus. This maybe facilitated by delivering information relating to the locus toproviders in the adjacent regions. As to a locus whose order acceptancerate is under 80%, if the provider-requester ratio is above 10, thereason for the low order acceptance rate may be something other than alow provider-requester ratio, and it may be unbeneficial to encourageproviders from adjacent regions to enter and service the locus.

In summary, a locus with low order acceptance rate and lowprovider-requester ratio may have a mismatch between the supply anddemand. It is presented in Table 4:

TABLE 4 Acceptance Provider-requester Number Number Number rate(numberof ratio(number of of of of acceptance/ providers/ orders acceptanceproviders number of orders) number of orders) 22 7 140 0.318182 6.36363633 12 299 0.363636 9.060606 31 11 275 0.354839 8.870968

These 3 vehicle demand dense loci illustrated in Table 4 may beconsidered as critically unhealthy (imbalanced of supply and demands),and providers may be encouraged to enter and service the loci. This maybe facilitated by delivering information relating to the loci toproviders, e.g., those in the adjacent regions.

While the foregoing has described what are considered to constitute thepresent teachings and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. It is intended by the following claims toclaim any and all applications, modifications and variations that fallwithin the true scope of the present teachings.

1. A system having at least one processor, storage, and a communicationplatform, comprising: a collection module configured to receive aplurality of orders for a service; an identification module configuredto mark a locus based on the plurality of orders, the marked locusrelating to a first number of orders of the plurality of orders, thefirst number of orders sharing a first characteristic, and the markedlocus relating to a first location; and a determination moduleconfigured to identify at least one provider of the service to whominformation relating to the marked locus is to be delivered, wherein thesystem is adapted for managing supply of the service.
 2. The system ofclaim 1, wherein the collection module is configured to receive at leastone piece of information selected from the group consisting of an orderlocation relating to an order of the plurality of orders, a providerlocation relating to a provider, a time relating to an order placed, atime for pickup relating to an order of the plurality of orders, anorder acceptance rate relating to the plurality of orders, an orderacceptance rate relating to the marked locus, a traffic conditionrelating to the marked locus, a road condition relating to the markedlocus, a weather condition relating to the marked locus, and historicalinformation relating to the marked locus.
 3. The system of claim 1,wherein the collection module comprises a location information collectorconfigured to identify the order location relating to an order of theplurality of orders.
 4. The system of claim 3, wherein the locationinformation collector comprises a receiver configured to communicatewith a positioning device relating to the order.
 5. The system of claim4, wherein the positioning device comprises a smart phone, a globalpositioning system, a laptop, a tablet computer, an in-vehicle computingplatform, a cloud computing based portable user platform with locationdetermined services, a personal digital assistant (PDA), a netbook, anultrabook, a digital photo frame, a media player, a handled gamingconsole, an ebook reader (e.g., Amazon kindle voyage, etc.), a globalnavigation satellite system (GLONASS), a Beidou navigation system(BDNS), a Galilio positioning system, a quasi-zenith satellite system(QZSS), a base station (BS), a wearable computing device (e.g.,eyeglasses, wrist watch, etc.), a virtual display device, a displayenhanced device, a car PC, a car navigation, a radar chronograph, or alaser velocimeter.
 6. The system of claim 1, wherein at least one orderof the plurality of orders comprises location information of arequester, the location information being determined based on apositioning signal from a device associated with the requester.
 7. Thesystem of claim 1, wherein at least one order of the plurality of ordersis received from a network.
 8. The system of claim 7, wherein thenetwork comprises Internet.
 9. The system of claim 1, wherein the firstcharacteristic is that a distance between the first location and alocation relating to an order of the marked locus is less than a firstthreshold.
 10. The system of claim 1, wherein the identification moduleor the determination module comprises at least one unit selected fromthe group consisting of a historic information processor, an orderinformation processor, a provider information processor, and acontingent information processor.
 11. The system of claim 10, whereinthe order information processor or the provider information processorcomprises at least one unit selected from the group consisting of alocation information processor, a distance calculator, a timecalculator, and a miscellaneous information unit.
 12. The system ofclaim 1, wherein the identification module is further configured toidentify the marked locus based on at least one cluster algorithm. 13.The system of claim 12, wherein the cluster algorithm comprises CLARANS,PAM, CLATIN, CLARA, DBSCAN, BIRCH, OPTICS, WaveCluster, CURE, CLIQUE,K-means algorithm, and hierarchical algorithm.
 14. The system of claim1, wherein the identification module is further configured to identify asecond number of providers relating to the marked locus, the secondnumber of providers sharing a second characteristic.
 15. The system ofclaim 14, wherein the second characteristic is that a distance betweenthe first location and a location relating to a provider of the secondnumber of providers is less than a second threshold.
 16. The system ofclaim 14, wherein the identification module is configured to mark thelocus based on a determination that the ratio of the first number to thesecond number exceeds a third threshold.
 17. The system of claim 1,wherein the identification module is further configured to identify anarea, and wherein the locus is marked based on one or more orders of theplurality of orders, the one or more orders relating to the area. 18.The system of claim 1, wherein the identification module is configuredto mark the locus based on a determination that the first number exceedsa fourth threshold.
 19. The system of claim 1, wherein the determinationmodule is configured to calculate a distance between the first locationand a location relating to the at least one provider, and wherein thedistance is less than a fifth threshold.
 20. The system of claim 1,wherein the determination module is configured to calculate a time forthe at least one provider to travel to the first location, and whereinthe time is less than a sixth threshold. 21-44. (canceled)